Application of scatter and attenuation correction to emission tomography images using inferred anatomy from atlas

ABSTRACT

A method of applying scatter and attenuation correction to emission tomography images of a region of interest of a patient under observation comprises the steps of aligning a three-dimensional computer model representing the density distribution within the region of interest with the emission tomography images. The computer model is created from image data of other subjects thereby to avoid the need to image the subject under observation to create the computer model. Scatter and attenuation correction is applied to the emission tomography images using the aligned computer model as a guide.

CROSS-REFERENCE TO RELATED APPLICATION

The present application claims the benefit of U.S. Provisional PatentApplication No. 60/096,649 filed on Aug. 14, 1998.

TECHNICAL FIELD

The present invention relates to emission tomography and in particularto a method and apparatus for applying scatter and attenuationcorrection to emission tomography images using anatomy inferred from anatlas.

BACKGROUND ART

Single Photon Emission Computed Tomography (SPECT) and Positron EmissionTomography (PET) are nuclear medicine diagnostic imaging techniques usedto measure the three-dimensional distribution of a radiopharmaceuticalwithin the body. Brain SPECT and PET imaging techniques are primarlyused to measure regional cerebral blood flow in a patient injected witha radiopharmaceutical to assist in the evaluation of stroke and thediagnosis of dementias such as Alzheimer's disease.

Although SPECT and PET are useful imaging techniques, their poorquantitative accuracy has been an obstacle in the ability to achieveincreased diagnostic reliability. Quantitative accuracy of brain SPECTand PET imaging has however been improved significantly through theapplication of scatter and attenuation correction to SPECT and PET brainimages. To be sufficiently accurate, the application of scatter andattenuation correction to SPECT and PET brain images must be guided bythe distribution of density within the head. Unfortunately, thedistribution of density within the head cannot be obtained from SPECTand PET brain scans and therefore, separate measurements are required.

Transmission imaging has been used to measure the distribution ofdensity within the head to allow scatter and attenuation correction tobe applied to SPECT and PET brain images. Unfortunately, the hardwarenecessary for making transmission measurements is complex, unreliableand requires extensive maintenance. Also, the need to make transmissionimaging measurements in addition to the SPECT or PET brain images,increases the time required to complete the overall imaging procedure.SPECT and PET imaging procedures are themselves lengthy and require apatient to remain motionless to ensure accurate brain images. For sickand elderly patients, this is a difficult task. Adding to the length ofthe imaging procedure increases the likelihood that patients will notremain motionless. Movement of a patient during the transmission imagingprocedure results in inaccurate measurements of the distribution ofdensity within the head. This of course provides an inaccurate guide forthe application of scatter and attenuation correction to SPECT and PETbrain images. Accordingly, improved methods to increase the diagnosticreliability of emission tomography images are desired.

It is therefore an object of the present invention to provide a novelmethod and apparatus for applying scatter and attenuation correction toemission tomography images.

DISCLOSURE OF THE INVENTION

Broadly stated, the present invention provides a method and apparatusfor applying scatter and attenuation correction to emission tomographyimages which estimates or “infers” the distribution of density within aregion of interest of a patient under observation using athree-dimensional computer model of the region of interest. It has beenfound that scatter and attenuation correction guided by a computer modelof the region of interest under observation produces results similar tothose when using transmission images of the actual region of interest asthe guide to the application of scatter and attenuation correction.

According to one aspect of the present invention there is provided amethod of applying scatter and attenuation correction to emissiontomography images of a region of interest of a subject under observationcomprising the steps of:

aligning a three-dimensional computer model representing the densitydistribution within said region of interest with said emissiontomography images; and

applying scatter and attenuation correction to said emission tomographyimages using said aligned computer model as a guide.

In a preferred embodiment, the computer model is in the form of atwo-component atlas. During the aligning step, a functional component ofthe atlas is firstly aligned with the emission tomography images togenerate a set of spatial transformation parameters. Following this, ananatomical component of the atlas is aligned with the emissiontomography images using the set of spatial transformation parameters.

The atlas may be selected from a database of atlases based on degree ofregistration with the emission tomography images. Alternatively,multiple atlases maybe combined to yield a resultant atlas which betterregisters with the emission tomography images.

According to another aspect of the present invention there is providedin an emission tomography imaging system where emission tomographyimages of a region of interest of a subject are taken for analysis andare corrected for scatter and attenuation, the improvement comprising:

using a three-dimensional computer model approximating the densitydistribution within the region of interest as a guide to the applicationof scatter and attenuation correction.

According to still yet another aspect of the present invention there isprovided an emission tomography image processing system comprising:

memory storing emission tomography images of a region of interest of asubject;

said memory also storing at least one three-dimensional computer modelof said region of interest, said computer model representing the densitydistribution within said region of interest; and

a processor for registering said computer model with said emissiontomography images and for applying scatter and attenuation correction tosaid emission tomography images using said registered computer model asa guide.

According to still yet another aspect of the present invention there isprovided an emission tomography imaging system comprising:

means for taking emission tomography images of a region of interest of asubject to form a three-dimensional image of said region of interest;

memory to store said emission tomography images, said memory alsostoring at least one three-dimensional computer model of said region ofinterest, said computer model representing the density distributionwithin said region of interest; and

a processor for aligning said computer model with said emissiontomography images and for applying scatter and attenuation correction tosaid emission tomography images using said aligned computer model as aguide.

According to still yet another aspect of the present invention there isprovided a computer readable medium including computer program code forapplying scatter and attenuation correction to emission tomographyimages of a region of interest of a subject, said computer readablemedium including:

computer program code for aligning a three-dimensional computer modelrepresenting the density distribution within said region of interestwith said emission tomography images; and

computer program code for applying scatter and attenuation correction tosaid emission tomography images using said aligned computer model as aguide.

The present invention provides advantages in that by using athree-dimensional computer model of the region of interest of a subjectunder observation that approximates its density as a guide for theapplication of scatter and attenuation correction to emission tomographyimages, the need for transmission imaging is obviated. Therefore, in thecase of SPECT and PET imaging, the imaging procedures do not need to belengthened. Also, since the distribution of density in the region ofinterest under observation is approximated by a three-dimensionalcomputer model, additional hardware is not required to create the guidefor the application of scatter and attenuation correction. This makesthe present invention significantly less expensive and more flexiblethan transmission imaging systems. In addition, since athree-dimensional computer model of the region of interest underobservation is used as the guide for the application of scatter andattenuation correction, scatter and attenuation correction can beapplied retrospectively to existing databases which include significantnumbers of emission tomography images for which no transmission imagingmeasurements were acquired.

BRIEF DESCRIPTION OF THE DRAWINGS

An embodiment of the present invention will now be described more fullywith reference to the accompanying drawings in which:

FIG. 1a shows a two-dimensional emission tomography brain image withoutthe application of scatter and attenuation correction;

FIG. 1b shows the two-dimensional image of FIG. 1a with the applicationof scatter and attenuation correction;

FIG. 1c is a two-dimensional transmission brain image;

FIGS. 1d and 1 e are two-dimensional emission tomography brain imageswith appurtenant anatomy derived from transmission images appliedthereto;

FIG. 2 is a block diagram showing a method for applying scatter andattenuation correction to emission tomography images in accordance withthe present invention;

FIG. 3a shows a two-dimensional emission tomography brain image of ahead phantom with scatter and attenuation correction;

FIG. 3b is a two-dimensional transmission brain image;

FIGS. 3c and 3 d are two-dimensional emission tomography brain imageswith appurtenant anatomy derived from transmission images appliedthereto;

FIGS. 3e and 3 f are two-dimensional emission tomography brain imageswith inferred anatomy derived from an atlas applied thereto;

FIGS. 4 and 5 show two-dimensional brain images and a graph comparinguniform appurtenant anatomy, uniform inferred anatomy and brain contour;

FIG. 6 shows two-dimensional brain images with non-uniform appurtenantand non-uniform inferred anatomy applied thereto;

FIGS. 7 and 8 show quantitative evaluation of head phantom SPECTreconstructions;

FIG. 9 shows two-dimensionl SPECT brain images;

FIG. 10 shows SPECT brain images superimposed onto transmissionreconstruction and inferred anatomy;

FIG. 11 shows profiles taken through reconstructions of a SPECT brainimage;

FIG. 12 is a graph showing a comparison of regional cerebral blood flowfrom reconstructed SPECT images guided by inferred anatomy andtransmission scans; and

FIG. 13 is a graph showing the correlation between the reconstructedSPECT images of FIG. 12.

BEST MODE FOR CARRYING OUT THE INVENTION

During emission tomography imaging such as for example SPECT and PET, apatient is injected with a radiopharmaceutical. Two-dimensional imagesor projections of a region of interest (ROI) of a patient are takenalong an arbitrary axis. The projections are digitized and conveyed to acomputer for storage in a three-dimensional array in computer memory toreconstruct a three-dimensional image of the region of interest. Oncethe three-dimensional image is reconstructed, two-dimensional slices ofthe region of interest can be viewed along virtually any arbitrary axisusing conventional software. FIG. 1a shows a two-dimensional emissiontomography brain image.

During the imaging procedure, the emissions from the radiopharmaceuticalare scattered and/or attenuated by different density tissue, aircavities and/or bones in the region of interest under observation. Ascan be seen in FIG. 1a, scattering and attenuation ofradiopharmaceuticals affects quantitative image quality. As a result,acquired emission tomography images are often unreliable. Applyingscatter and attenuation correction to emission tomography images, usingtransmission images of the same region of interest taken during the sameimaging procedure, is known but suffers from the disadvantages discussedpreviously. FIG. 1b shows the two-dimensional image of FIG. 1a withscatter and attenuation correction applied using transmission imagestaken of the same anatomy. FIG. 1c shows an example of a transmissionimage and FIGS. 1d and 1 e show appurtenant anatomy (sagittal andtransverse respectively) derived from the transmission images applied toemission tomography brain images.

In the present invention, a three-dimensional computer model or “atlas”of the region of interest, which provides accurate density distributionof the region of interest, is stored in a database in computer memoryand is used as a guide for the application of scatter and attenuationcorrection to emission tomography images. In the present embodiment, theatlas includes two components, namely a functional component simulatinga SPECT OR PET scan of the region of interest and an anatomicalcomponent simulating a transmission scan of the region of interest. Theatlas can be created from existing transmission images or x-ray CT scansof similar regions of interest from other patients and averaged to forma suitable computer model or atlas. Multiple models for each region ofinterest and models for different regions of interest can be stored inthe database and accessed individually during scatter and attenuationcorrection of emission tomography images. Since a computer model of theregion of interest is used, additional hardware and procedure time isnot required to apply scatter and attenuation correction to emissiontomography images. An example of the application of scatter andattenuation correction to emission tomography brain images in accordancewith the present invention will now be described.

Referring now to FIG. 2, a block diagram illustrating the present methodfor applying scatter and attenuation correction to emission tomographybrain images is shown. Initially, emission tomography brain images of apatient (block 50) are acquired in a known manner. The acquired brainimages are in the form of two-dimensional projections of theradiopharmaceutical distribution in the brain.

Initially, a preliminary reconstruction of the acquired brain images isperformed to digitize and assemble the two-dimensional projections intoa three-dimensional array in computer memory to create athree-dimensional image of the brain (block 52). An atlas of the head(the region of interest in this case) is then downloaded from thedatabase into a datafile (block 54) and the functional component of thehead atlas is identified. Once identified, the functional component ofthe head atlas, in this embodiment the brain component, is copied to athree-dimensional array. Following this, an alignment procedure toregister spatially the brain component of the head atlas with thethree-dimensional brain image is performed (block 56). In the presentembodiment, a simplex algorithm is used to register spatially, the braincomponent of the head atlas with the brain image as described in“Numerical Recipes In C” by Press et al, 2^(nd) edition, New York, N.Y.,Cambridge University Press, 1992. Those of skill in the art will howeverappreciate that other registration algorithms can be used to align thebrain component of the head atlas to the three-dimensional brain image.During this alignment procedure, a set of 3D spatial transformationparameters representing the three-dimensional transformations, includingbut not limited to rotation, shifting and scaling, that are necessary toregister the brain component of the head atlas with thethree-dimensional brain image, is calculated and is stored in thecomputer memory as a matrix.

Once the set of 3D transformation parameters is calculated and stored,the 3D spatial transformation parameters in the set are applied to theanatomical component of the head atlas to register it with thethree-dimensional brain image (block 58). With the anatomical componentof the head atlas aligned with the three-dimensional brain image, theatlas can be used as a density distribution guide to the application ofscatter and attenuation correction to the three-dimensional brain image.

With an accurate density distribution guide established, scattercorrection is applied to the three-dimensional brain image followed byattenuation correction in a known manner (block 60). Once scatter andattenuation correction has been applied to the three-dimensional brainimage, the brain image is reconstructed into a three-dimensional arrayin computer memory to complete the image correction process (block 62).The corrected three-dimensional brain image can then be analyzed (block64). Appendix A includes pseudocode representing the above-identifiedprocess.

FIGS. 3a to 3 f show a comparison of two-dimensional brain images of ahead phantom with anatomy derived from transmission images (appurtenantanatomy) and anatomy derived from a head atlas (inferred anatomy)applied.

The present method and apparatus was tested using an anthropomorphichead phantom modeling soft tissue, hard tissue and air cavities within askull and including a two-compartment brain reservoir. The twocompartments of the reservoir were separately filled with two watersolutions of Tc-99m, having a specific activity ratio of 4:1. Fan-beamSPECT was acquired followed by a Tc-99m transmission scan sixty hourslater. The reconstructed transmission image is referred to asappurtenant anatomy.

Five scatter and attenuation correction schemes were evaluated based onnon-uniform appurtenant anatomy, uniform appurtenant anatomy,non-uniform inferred anatomy, uniform inferred anatomy, and uniformbrain contour. For uniform scatter and attenuation correction, theinferred and appurtenant anatomies were segmented and assigned uniformattenuation coefficients of soft tissue (0.15 cm⁻¹ for Tc-99m). A SPECTreconstruction of the head phantom was similarly processed to facilitatescatter and attenuation correction guided by brain contour. Scattercorrection was based on a non-stationary deconvolution scattersubtraction as described in the paper authored by Stodilka et alentitled “The Relative Contributions of Scatter and AttenuationCorrection Toward Brain SPECT Quantification”, Phys Med Biol, 1998.Attenuation correction/reconstruction was subsequently performed byordered-subsets expectation-maximization as set out in the paperauthored by Hudson HM et al entitled “Accelerated Image Reconstructionusing Ordered Subsets of Projection Data”, IEEE Trans Med Imaging 13601-609, 1994. Although particular examples of scatter correction,attenuation correction and reconstruction algorithms are described,those of skill in the art will appreciate that other scatter correction,attenuation correction and reconstruction methods can be used.

Uniform scatter and attenuation correction should be guided by thecontour of the attenuating medium if an acceptable level of accuracy forobjective diagnosis is to be expected. FIGS. 4 and 5 show that uniformappurtenant anatomy is better approximated by uniform inferred anatomythan by brain contour. As can be seen in FIG. 6, non-uniform appurtenantanatomy and non-uniform inferred anatomy are similar.

Head phantom SPECT reconstructions were quantitatively evaluated by fourmetrics, namely bias, uniformity, contrast-recovery, and relativequantification (see FIGS. 7 and 8). As will be appreciated, scatter andattenuation correction guided by inferred anatomy provides quantitativeaccuracy that is distinctively superior to scatter and attenuationcorrection guided by brain contour.

Application of scatter and attenuation correction to emission tomographyimages of anatomy other than the head can also be performed. Forexample, inferred anatomy can also be used to apply scatter andattenuation correction to cardiac images. During construction of thecardiac atlas, an anatomical component of the cardiac atlas representingthe anatomical features of the thorax is created and includes:

soft-tissues such as the heart, liver, muscle, and fat;

very low-density soft-tissues such as the lungs; and

high-density tissues such as bone and cartilage in the ribs and spine.

A functional component of the cardiac atlas is also created andsimulates a SPECT or PET cardiac image. Appropriate data to constructthe cardiac atlas can be obtained in a variety of ways including, butnot limited to, imaging a phantom or human subject by X-ray CT, MRI, orgamma-camera transmission computed tomography; or computer simulation.Also, the cardiac atlas can be constructed by amalgamating a pluralityof patient scans.

The procedure of using inferred cardiac anatomy to apply scatter andattenuation correction to cardiac images remains the same as with brainimaging described above. First, a preliminary reconstruction of thepatient's cardiac SPECT data is performed. The functional component ofthe cardiac atlas is then registered to the preliminary reconstruction.The registration includes a spatial transformation that may includeshifting, rotation, scaling, and/or non-linear operations such aswarping. The registration procedure calculates a matrix representing thespatial transformation that maps atlas space into patient-specificspace. Once the matrix has been calculated, the matrix is applied to theanatomical component of the cardiac atlas, thus inferring anatomy in thechest.

Although the above methods describe the use of a single generic atlas ofthe anatomy under observation, those of skill in the art will appreciatethat custom atlases can be developed and stored in the database. Forexample, disease specific atlases such as an Alzheimer's disease atlasor a stroke atlas can be developed and used when correcting emissiontomography images of patients suffering from those diseases. The diseasespecific atlases may be tracer or lesion specific to allow for localconcavities in radiopharmaceutical uptake. Patients with severeAlzheimer's or Pick's disease do not have normal cerebral blood flow.Areas with flow deficits can limit the accuracy of the registration ofthe emission tomography images with the functional component of the headatlas if the head atlas assumes normal blood flow and hence uniformradiopharmaceutical uptake. Atlases can also be developed to take intoaccount physical traits such as for example, an exceptionally largenasal sinus. During scatter and attenuation correction, the operator canselect the appropriate atlas to use. Alternatively, the atlas can beselected automatically by computer software. In this case, the software,performs a preliminary reconstruction using each custom atlas andregisters the atlas to the preliminary reconstruction to measure theaccuracy of the registration. The atlas with the highest registrationaccuracy is then selected. If desired, the software can use fuzzy logic,theoretical calculations or other criteria to combine two or moreatlases to yield a single resultant atlas, which provides a betterdegree of registration.

EXAMPLE Example 1 Inferred Anatomy and Brain Imaging

The following example is described for the purposes of illustration andis not intended to limit the scope of the present invention.

Inferring Anatomy From a Head Atlas

A head atlas was prepared as follows. A Zubal three-dimensionaldigitized MRI head phantom [Zubal et al 1994] was segmented to producetwo data sets, namely a SPECT atlas simulating a SPECT scan of thephantom, and an anatomical atlas simulating a transmission scan of thephantom. The SPECT atlas consisted of voxels containing gray-matter andwhite-matter, to which ^(99m)Tc-HMPAO relative uptakes of 4 and 1 hadbeen assigned respectively. The anatomical atlas consisted of voxelscontaining hard-tissue, soft-tissue and nasal sinus, to which thecorrespondent 140 keV narrow-beam attenuation coefficients of 0.25,0.15, and 0.075 cm⁻¹ were assigned.

A patient SPECT scan was then reconstructed without scatter andattenuation correction. Facial activity was removed to yield a data setreferred to as the preliminary patient reconstruction. The SPECT atlaswas then registered to the preliminary patient reconstruction and thespatial transformation was recorded. This transformation was thenapplied to the anatomical atlas, thus inferring the location of thepatient's soft-tissue, hard-tissue and air cavities (see FIG. 9).

A general-purpose radiological analysis program (Hermes, NuclearDiagnostics, Stockholm, Sweden) was used to perform the unimodalityregistration. The large variation in head orientation necessitated thata manual registration be first performed. This was followed by anautomated refinement. The cost function of the automated registrationwas defined as the sum of absolute count differences [Hoh et al 1993]. Aglobal minimum was sought by a simplex search [Nelder and Mead 1965]within a parameter space consisting of rotating, shifting, and linearscaling in x, y, and z directions [Holman et al 1991, Slomka et al1995].

Sequential Transmission and Emission Imaging

Ten dementia patients (5 females and 5 males, with a mean age of 64.3years) were analyzed. For each patient, a transmission scan was firstacquired. Patients became very relaxed during the quiet transmissionscan, and were then injected with 740 MBq of ^(99m)Tc-HMPAO. The SPECTprocedure was started approximately 5 minutes post-injection. The SPECTsystem, which has transmission capabilities, is described in detail[Kemp et al 1995]. It consists of a General Electric 400AC/T gammacamera (General Electric, Milwaukee, Wis.) with a 409.6 mm diametercircular field-of-view. Projections were acquired through a fan-beamcollimator (Nuclear Fields, Des Plaines, Ill.) having a 600 mm focallength and 1.5 mm flat-to-flat hexagonal hole width. The transmissioncomponent includes a frame mounted onto the camera's collimator thatholds a tantalum-collimated ^(99m)Tc line source along the focal line ofthe fan-beam collimator. Collimation of both the line source and cameraminimizes scatter. As a result, the transmission system effectivelymeasures narrow-beam attenuation coefficients [Tsui et al 1989, Kemp etal 1995]. SPECT and transmission scans were acquired with a 20% energywindow, centered on the ^(99m)Tc photopeak of 140 keV. The scansconsisted of 128 projections, equally spaced over 360°. Each circularprojection was acquired into a 128×128 pixel square matrix (1 pixel=3.2mm). Both transmission and SPECT scans were 10 seconds per projectionand count rates were approximately 70 and 1.5 kcounts per second,respectively. All scans were corrected for uniformity using 100 millioncount flood images, and transmission scans were normalized to 50 millioncount blank images. Radii of rotation varied among the patients; thesmallest being 170 mm and the largest being 205 mm. Prior toreconstruction, all scans were rebinned to object-plane parallel-holegeometry via two-dimensional cubic interpolation.

Scatter and Attenuation Correction and Reconstruction

The SPECT data were reconstructed using a maximum-likelihood estimatorwith an unregularized 32-level ordered subset [Hudson and Larkin 1994]implementation of the expectation maximization algorithm [Shepp andVardi 1982, Lange and Carson 1984] (OSEM). The four projections thatwere used per sub-iteration were equally spaced about 360°. Attenuationwas modelled in the matched projector/backprojector pair, and a scatterestimate [Stodilka et al 1998b] was added as an a priori backgroundfollowing forward projection [Lange and Carson 1984, Bowsher et at 1996,Kadrmas et al 1998]. Both scatter and attenuation modelling incorporatedthe narrow-beam attenuation coefficients from transmission imaging orinferred anatomy. Detector response was not included. Four iterations ofOSEM were used, following initialization with a uniform [Nunez andLlacer 1990] support prior derived from transmission reconstruction orinferred anatomy. Reconstructions were then post-filtered [Nunez andLlacer 1990] using a three-dimensional Butterworth filter with an orderof 8 and cutoff at 0.42 cm⁻¹. The transmission data were alsoreconstructed by the emission OSEM algorithm, following blank scannormalization and log-transformation.

Line source collimation, coupled with limitations in detector count ratecapability and patient compliance resulted in less than idealtransmission statistics. To reduce the effects of transmission imagingnoise propagation into the SPECT reconstruction [Xu et al 1991, Tung andGullberg 1994], the transmission reconstructions were segmented asfollows. Soft-tissue in the reconstructed transmission volumes wasforced to have uniform density. First, a large region of interest (ROI)was drawn around soft-tissue regions, and mean and variance estimateswere calculated. Then, all voxels having count densities within ±2standard deviations of this mean were assigned to 0.15 cm⁻¹. Thus, thetransmission reconstructions were characterized by noiselesssoft-tissue, yet featured hard-tissue and air cavities.

Template-Based Quantification

Previous work has identified that a major confound to reproduciblequantification, originates from manual and threshold-dependent placementof anatomical ROIs onto SPECT scans [Msaki et al 1998]. To reduce thissubjective source of error, a normal template [Msaki et al 1998] ontowhich twelve bilateral volumetric ROIs are demarcated [Karbe et al 1994]was used. The ability to store the template and its ROIs ensuresreproducibility of the analysis. This quantification procedure alsointroduces standardization to the analysis, which facilitates theexchange of data among different institutions [Evans et al 1988]. Allreconstructed scans were registered to the normal template, hereinreferred to as “spatial normalization”. Prior to spatial normalization,voxels previously identified as facial activity were set to zero.Following superposition of the template ROIs onto each scan, thecortical rCBF for each ROI was normalized to cerebellar rCBF [Karbe etal 1994] and corrected for blood flow-dependent tracer reflux [Lassen etal 1988]. Analysis of the absolute concentration of radiopharmaceuticalwas not performed since currently, absolute rCBF quantification isseldom used in SPECT [Bakker and Pauwels 1997].

Quantitative Error Analysis

A sample of inferred anatomy and transmission reconstruction isillustrated in FIG. 10. In FIG. 11, profiles through the SPECTreconstructions guided by transmission scans and inferred anatomy arepresented. The profiles have not been normalized to cerebellar countdensity.

The means and standard errors of regional cerebral blood flow from SPECTscans guided by inferred anatomy and transmission scans were comparedROI-by-ROI (see FIG. 12). Statistical analysis was also performedROI-by-ROI using repeated analysis of variance to determine where therewere significant differences between the inferred anatomy versustransmission-guided reconstruction methods. Pooled-ROI and ROI-dependentcorrelation coefficients were calculated between inferred anatomyreconstruction and transmission-guided SPECT reconstructions. FIG. 13shows the correlation between the two reconstruction and quantificationmethods by pooling all ROIs and patients together. The p value forsignificance was set to 0.05 for all tests.

Inferred Anatomy Error Propagation Analysis

Four sources of error were identified that contribute to discrepanciesin ROI quantification:

(1) inferred anatomy-guided scatter correction;

(2) inferred anatomy-guided attenuation correction;

(3) inferred anatomy-guided spatial normalization, which is equivalentto ROI misplacement; and

(4) patient motion between transmission and SPECT scans.

The first two are inherent limitations to the principle of inferredanatomy, whereas the last two represent artifactual exaggerations oferrors in the context of this example. The first three sources of error,namely scatter, attenuation, and ROI misplacement were measured.

The propagation of ROI quantification to differences between inferredanatomy scatter correction and transmission-guided scatter correctionwere analyzed by performing:

(1) inferred anatomy-derived scatter correction;

(2) transmission-derived attenuation correction; and

(3) applying the spatial normalization calculated to be optimal forregistering the transmission-derived SPECT reconstruction to thequantification template.

A similar analysis for evaluating the effects of inferredanatomy-derived attenuation correction was carried out by performing:

(1) transmission -derived scatter correction;

(2) inferred-anatomy derived attenuation correction; and

(3) applying the spatial normalization calculated to be optimal forregistering the transmission-derived SPECT reconstruction to thequantification template.

The effects of ROI misplacement were quantified by performingtransmission-derived scatter and attenuation correction, and thenapplying the spatial transformation calculated to be optimal forregistering the inferred anatomy-derived SPECT reconstruction to thequantification template. Thus, the full propagation analysis resulted inthree reconstructions, each of which was quantitatively compared (viathe above-described template-based quantification procedure) with the“gold-standard” transmission-derived SPECT reconstruction and spatialnormalization. This procedure was performed for the ten patients, andthe results averaged. However, once the errors due to scatter,attenuation and ROI misplacement were separately quantified, theirtotals were found to be less than the errors caused by the full inferredanatomy protocol. This additional source of error is termed“unaccountable” in Table 2, and is believed to be caused by patientmotion.

Qualitative Analysis

A sample comparison of inferred anatomy and transmission reconstructionis shown in FIG. 10 for mid-sagittal and cortical-axial slices. Goodreproduction of soft-tissue and hard-tissue at the cortical level isnoted. Some discrepancy is seen near the vertex; however, this region isseldom included in quantitative analysis. Discrepancies near the nasalsinus are most marked. Fortunately, these structures mostly involvelow-density areas such as air, and to a much lesser degree, soft-tissuesand cartilage, which do not scatter and attenuate photons as much ashigher density structures.

Mid-brain profiles, shown in FIG. 11, compare a SPECT reconstructionguided by transmission imaging with the same SPECT reconstruction guidedby inferred anatomy. The profile was taken along the longest axis of thehead, which is most sensitive to mis-registered transmission mapsfollowing scatter and attenuation correction [Huang et al 1979].

Quantitative Error Analysis

Table 1 below shows the results of the repeated analysis of variance andcorrelation analysis comparing transmission reconstruction andquantitative and inferred anatomy-guided reconstruction andquantification. Left frontal and central sulcus ROIs showed the highestprobability of a true difference, reaching statistical significance withp=0.001 and 0.002, respectively. These ROIs also had the highestcorrelation coefficients relating transmission reconstruction andquantification and inferred anatomy-guided SPECT reconstruction andquantification. This increased correlation may be an artifact of theincreased differences in the ROI means. Interestingly, the left frontaland central sulcus ROIs were also found to have marked rCBF deficits,suggesting that inferred anatomy may have difficulties near regions withsubstantially reduced radiopharmaceutical uptake.

FIG. 12 shows the means and standard errors for transmissionreconstruction and inferred anatomy in each of the 12 ROIs, averagedacross the entire population. The mean absolute difference for all ROIsacross the whole population was 7.5%. Correlation for all ROIs and allpatients was found to be high: r′=0.92 as illustrated in FIG. 13.

TABLE 1 ANOVA Paired sample Paired sample Region Significancecorrelation Significance Left frontal 0.001 0.965 <0.001 Right frontal0.088 0.953 <0.001 Left central sulcus 0.002 0.957 <0.001 Right centralsulcus 0.234 0.937 <0.001 Left parietal 0.092 0.772 0.009 Right parietal0.473 0.862 0.001 Left temporal 0.957 0.939 <0.001 Right temporal 0.3970.835 0.003 Left occipital 0.271 0.835 0.003 Right occipital 0.073 0.8580.001 Left cerebellar 0.545 0.938 <0.001 Right cerebellar 0.603 0.925<0.001

Error Propagation Analysis

The results from the propagation analysis are presented in Table 2below. These results are presented as errors relative to the“gold-standard” transmission reconstruction-guided reconstruction andspatial normalization. Three sources of error due to inferring anatomywere analyzed namely, errors in scatter distribution estimates, errorsdue to misguided attenuation compensation, and errors due toregion-of-interest misplacement. The error components, averaged acrossten patients, are shown for each of the twelve bilateralregions-of-interest. The table summarizes the percentage that each ofthese error sources contributed to the total quantitative differencesbetween SPECT reconstructions guided by transmission scans or inferredanatomy. The fourth numerical column indicates the percentage of totaldifferences that could not be accounted for by inferring anatomy.

On average, it was found that errors in scatter distribution estimatesresults in approximately 10.0% of the total quantitative error;attenuation correction: 36.6%, and ROI misplacement: 27.0%. The relativecontributions of inferred anatomy-derived scatter and attenuationcorrection to the total error seem credible. Compensating forattenuation is of considerably greater consequence than removingscattered photons [Rosenthal et al 1995]. Approximately 26.5% of thetotal discrepancy between inferred anatomy and transmission imagingcould not be accounted for in the error propagation analysis of scatter,attenuation, and ROI misplacement but is believed to be as a result ofpatient motion during data acquisition.

TABLE 2 Scatter Attenuation Region Unac- correction correction misplace-countable Region % % ment % error % Left frontal 6.1 44.5 28.6 20.8Right frontal 12.0 49.9 29.4 8.7 Left central sulcus 5.7 40.4 17.9 36.0Right central sulcus 6.3 34.3 16.0 43.4 Left parietal 7.9 25.7 30.3 36.1Right parietal 10.0 34.1 25.9 30.0 Left temporal 10.0 38.3 20.0 31.8Right temporal 13.8 27.0 30.7 28.5 Left occipital 13.2 23.2 27.6 36.0Right occipital 8.3 22.5 29.0 40.2 Left cerebellar 13.5 48.7 31.9 5.8Right cerebellar 12.5 50.2 36.4 0.8 Average 10.0 36.6 27.0 26.5

Qualitative and Quantitative Comparisons

Comparing inferred anatomy with transmission reconstructions indicatedgood reproduction of soft-tissue and hard-tissue in cortical areas forall ten patients. However, in many scans, a discrepancy was indicatednear the sinus cavity, as shown in FIG. 10. Despite this, inferredanatomy is more robust and accurate in providing estimates of underlyingtissue distribution than fitting ellipses to photopeak emission data.The technique of fitting ellipses depends on adequate facial activitysince the contours of the brain and head differ so considerably at thelevel of the cerebellum. Facial activity should not form the basis forestimating underlying tissue distributions since uptake varies withradiopharmaceutical and time between injection and scanning [Leveille etal 1992, Van Dyck et al 1996], making it an unreliable dependency.Parenthetically, the qualitative similarities demonstrated betweeninferred anatomy and transmission reconstruction indicate confidence inaccurately guiding scatter and attenuation correction. However, it isimportant to note that similar shape is a sufficient, but not necessary,prerequisite for accurate scatter and attenuation correction [Welch etal 1997, Natterer 1993].

Slight truncation effects are noticed on the transmission images forthree of the ten scans. Truncation occurred for kyphotic patients withbroad shoulders or with short necks. This limitation was generallyrestricted to transaxial slices below the level of the cerebellum orvery near its base, where the gamma camera's circular field-of-viewproved to be inconvenient. The truncation only involved nasal cartilage,and is therefore not expected to significantly impact results, as isdemonstrated in the quantitative accuracy exhibited at the cerebellarlevel (see FIG. 12).

Inferred anatomy had difficulties in regions with marked rCBF deficit,such as the left frontal lobe. Although frontal lobes generally exhibithigh variability in HMPAO uptake [Deutsch et al 1997], only the leftfrontal lobe had statistically significant error. This suggests thatregional quantitative errors incurred by inferred anatomy are associatedwith rCBF deficits. However, previous work demonstrates that they mayalso be sensitive to spatial region. Achieving good quantitativeaccuracy in extended sources is often difficult and this is particularlytrue with peripheral ROIs, where reconstructed activity is mostsensitive to misregistration of the attenuating medium [Huang et al1979]. For example, in the brain a mismatch between emission andtransmission data no greater than 5 mm can produce a 10% error in a 10mm thick peripheral cortical ROI [Andersson et al 1995b, Huang et al1979]. In general, extended sources, such as the brain, are affected bymisregistration more than compact sources, such as the heart [Anderssonet al 1995b, McCord et al 1992, Ter-Pogossian 1992, Matsunari et al1998]. Since the brain is elliptical, it is expected that regions alongthe periphery of the long axis of the head will be more sensitive toattenuation map misregistration than those along the short axis.

Although a preferred embodiment of the present invention has beendescribed, those of skill in the art will appreciate that variations andmodifications may be made to the present invention without departingfrom the spirit and scope thereof as defined by the appended claims.

APPENDIX A emis_proj = //initialize 3D array. Projections are 2D, but wehave //many projections. emis_proj_sc = //init 3D array. Storeprojections here after scatter //correction. emis_proj_sc_ac = //init 3Darray. Store projections here after scatter and //attenuationcorrection. emis_reco_sc_ac = //init 3D array. The reconstructed 3Dbrain scan. tx_proj = //init 30 array. Transmission projections ofanatomy. anatomy = //init 3D array. The 3D distribution of anatomy goes//here. emis_proj = do_patient_emission_scan; // first step is toacquire the patient emission scan. // The acquire data is in the form ofprojections of the // radiopharmaceutical distribution. // Theseprojections require scatter and attenuation // correction. // In orderto perform these corrections, an estimate of // anatomy must beobtained. Current, two methods of // obtaining an anatomy estimate arepossible: if (infer==1)  anatomy = infer_anatomy(emis_proj); // This isour method. See the // function, below. This is what // we arepatenting. elseif (infer==0) { // Alternatively, we acquire transmissionprojections of // the patient. // This is very similar to x-ray CT. tx_proj = do_patient_transmission_scan; anatomy = reconstruct(tx_proj);// These projections are // reconstructed into a 3D // distribution ofanatomy. Note // that this reconstruction is // almost identical to the// emission reconstruction; // however, transmission // reconstructionsdo not // require scatter or attenuation // correction. } // After wehave our emission projections and an anatomy // estimate, wesequentially apply scatter correction and // attenuation correction tothe emission data. emis_proj_sc = scatter_correction(emis_proj,anatomy);// Perform the scatter correction on the // emission_data. // Thescatter correction requires knowing the // anatomy. emis_proj_sc_ac =attenuation_correction(emis_proj_sc,anatomy); // Perform the attenuationcorrection on the // emission data that has been scatter corrected. //The attenuation correction requires knowing the // anatomy.emis_reco_sc_ac = reconstruct(emis_proj _sc_ac); // After correcting theemission data for scatter // and attenuation, perform the emission //reconstruction. Stop function aligned_head_atlas =infer_anatomy(emis_proj); { emis_reco = // init 3D array. This will holda preliminary emission // reconstruction that has NOT been corrected forscatter // or attenuation. head_atlas = // init 3D array. This will holdthe full head // atlas (brain, skull, soft tissue). The head // atlas isin its original orientation. head_atlas_brain = // init 3D array. Thiswill hold the brain // component of the head atlas. This brain //component will be in its original // orientation. alignment_par = // aset of parameters that represent the 3D // transformations (including,but not limited to: // rotation, shifting, scaling) necessary to align// head_atlas_brain to emis_reco. These // transformations will beapplied to head_atlas. aligned_head_atlas = // init 3D array. Thealignment parameters // are applied to head_atlas. This variable // isthe function's output. emis_reco = reconstruct(emis_proj) // Perform apreliminary // reconstruction of the emission // data. head_atlas =load_3D_data(atlas filename); // Load the head // atlas.head_atlas_brain = extract(head_atlas,brain); // Identify and // extractthe brain // from the head // atlas. alignment_par =find_optimal_alignment(emis_reco,head_atlas_brain); // Calculate the 3Dtransformation parameters // to align head_atlas_brain to emis_reco. //This procedure calculates the optimal set // of transformationparameters. In medical // science, this alignment is known as //“registration”. aligned_head_atlas =apply_alignment(head_atlas,alignment_par) ; // Apply the alignment tohead_atlas. // aligned_head_atlas // represents an anatomy estimate thatis // used for guiding the scatter and // attenuation corrections. (Notethat the // variable aligned_head_atlas is passed back // as anatomy tothe main program.) Also note // that, although the set of transformation// parameters that was calculated represents // the optimal set foraligning // head_atlas_brain to emis_reco, these same // parameters arenow applied to head_atlas. }

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What is claimed is:
 1. A method of applying scatter and attenuationcorrection to emission tomography images of a region of interest of asubject under observation comprising the steps of: aligning athree-dimensional computer model in the form of a two-component atlasrepresenting the density distribution within said region of interestwith said emission tomography images, said computer model being createdfrom image data of other subjects thereby to avoid the need to imagesaid subject under observation to create said computer model; andapplying scatter and attenuation correction to said emission tomographyimages using said aligned computer model as a guide.
 2. The method ofclaim 1 wherein during said aligning step, a functional component ofsaid atlas is firstly aligned with said emission tomography images togenerate a set of spatial transformation parameters and thereafter, ananatomical component of said atlas is aligned with said emissiontomography images using said set of spatial transformation parameters.3. The method of claim 2 wherein said functional component simulates aSPECT or PET scan of said region of interest and wherein said anatomicalcomponent simulates a transmission scan of said region of interest. 4.The method of claim 3 wherein said region of interest is the head andwherein said functional component is the brain component of a headatlas.
 5. The method of claim 3 wherein said region of interest is theheart, said functional component of said atlas simulating a cardiacimage and said anatomical component of said atlas representinganatomical features of the thorax.
 6. The method of claim 5 wherein theanatomical features of the thorax include: soft-tissues such as theheart, liver, muscle, and fat; very low-density soft-tissues such as thelungs; and high-density tissues such as bone and cartilage in the ribsand spine.
 7. The method of claim 2 further comprising the step ofselecting an atlas from a database of atlases prior to performing saidaligning step.
 8. The method of claim 7 wherein said selecting step isperformed manually.
 9. The method of claim 7 wherein said selecting stepis performed automatically based on the degree of registration of eachatlas in said database with said emission tomography images.
 10. Themethod of claim 9 wherein the degree of registration is determined by:performing a preliminary reconstruction of each atlas; and registeringthe atlas to the preliminary reconstruction.
 11. The method of claim 9further comprising the step of combining multiple atlases to yield aresultant atlas that better registers with said emission tomographyimages.
 12. The method of claim 7 wherein said database includes diseasespecific atlases, physical trait specific atlases and/or tracer orlesion specific atlases.
 13. The method of claim 1 wherein said computermodel is created from transmission images or x-ray CT scans of theregion of interest of other subjects.
 14. The method of claim 13 whereinsaid transmission images or x-ray CT scans are taken from a variety ofother subjects and averaged thereby to create said computer model. 15.The method of claim 14 wherein during said aligning step, a functionalcomponent of said atlas is firstly aligned with said emission tomographyimages to generate a set of spatial transformation parameters andthereafter, an anatomical component of said atlas is aligned with saidemission tomography images using said set of spatial transformationparameters.
 16. The method of claim 15 wherein said functional componentsimulates a SPECT or PET scan of said region of interest and whereinsaid anatomical component simulates a transmission scan of said regionof interest.
 17. An emission tomography imaging method where emissiontomography images of a region of interest of a subject are taken foranalysis and are corrected for scatter and attenuation, the methodfurther comprising the step of: using a three-dimensional computer modelin the form of a two-component atlas approximating the densitydistribution within the region of interest as a guide to the applicationof scatter and attenuation correction, said computer model being createdfrom image data of other subjects thereby to avoid the need to imagesaid subject to create said computer model.
 18. The emission tomographyimaging method of claim 17 wherein during said aligning step, afunctional component of said atlas is firstly aligned with said emissiontomography images to generate a set of spatial transformation parametersand thereafter, an anatomical component of said atlas is aligned withsaid emission tomography images using said set of spatial transformationparameters.
 19. The emission tomography imaging method of claim 18wherein said functional component simulates a SPECT or PET scan of saidregion of interest and wherein said anatomical component simulates atransmission scan of said region of interest.
 20. The method of claim 17wherein said computer model is created from transmission images or x-rayCT scans of the region of interest of other subjects.
 21. The method ofclaim 20 wherein said transmission images or x-ray CT scans are takenfrom a variety of other subjects and averaged thereby to create saidcomputer model.
 22. The method of claim 21 wherein during said aligningstep, a functional component of said atlas is firstly aligned with saidemission tomography images to generate a set of spatial transformationparameters and thereafter, an anatomical component of said atlas isaligned with said emission tomography images using said set of spatialtransformation parameters.
 23. The method of claim 22 wherein saidfunctional component simulates a SPECT or PET scan of said region ofinterest and wherein said anatomical component simulates a transmissionscan of said region of interest.
 24. An emission tomography imageprocessing system comprising: memory storing emission tomography imagesof a region of interest of a subject under observation; said memory alsostoring at least one three-dimensional computer model of said region ofinterest, said computer model being in the form of a two-component atlasand representing the density distribution within said region ofinterest, said computer model being created from image data of othersubjects thereby to avoid the need to image said subject underobservation to create said computer model; and a processor forregistering said computer model with said emission tomography images andfor applying scatter and attenuation correction to said emissiontomography images using said registered computer model as a guide. 25.An emission tomography image processing system as defined in claim 24wherein said processor firstly registers a functional component of saidatlas with said emission tomography images to generate a set of spatialtransformation parameters and then registers an anatomical component ofsaid atlas with said emission tomography images using said set ofspatial transformation parameters.
 26. An emission tomography imageprocessing system as defined in claim 25 wherein said functionalcomponent simulates a SPECT or PET scan of said region of interest andwherein said anatomical component simulates a transmission scan of saidregion of interest.
 27. An emission tomography image processing systemas defined in claim 26 wherein said memory stores a database of atlases.28. An emission tomography image processing system as defined in claim27 wherein said processor selects an atlas from said databaseautomatically based on the degree of registration of each atlas in saiddatabase with said emission tomography images.
 29. An emissiontomography image processing system as defined in claim 28 wherein saidprocessor performs a preliminary reconstruction of each atlas andregisters the atlas to the preliminary reconstruction to determine thedegree of registration of each atlas.
 30. An emission tomography imageprocessing system as defined in claim 28 wherein said processor combinesmultiple atlases to yield a resultant atlas that better registers withsaid emission tomography images.
 31. An emission tomography imageprocessing system as defined in claim 27 wherein said database includesdisease specific atlases, physical trait specific atlases and/or traceror lesion specific atlases.
 32. An emission tomography imaging systemcomprising: means for taking emission tomography images of a region ofinterest of a subject under observation to form a three-dimensionalimage of said region of interest; memory to store said emissiontomography images, said memory also storing at least onethree-dimensional computer model of said region of interest, saidcomputer model being in the form of a two-component atlas andrepresenting the density distribution within said region of interest,said computer model being created from image data of other subjectsthereby to avoid the need to image said subject under observation tocreate said computer model; and a processor for aligning said computermodel with said emission tomography images and for applying scatter andattenuation correction to said emission tomography images using saidaligned computer model as a guide.
 33. A computer readable mediumincluding computer program code for applying scatter and attenuationcorrection to emission tomography images of a region of interest of asubject under observation, said computer readable medium including:computer program code for aligning a three-dimensional computer modelrepresenting the density distribution within said region of interestwith said emission tomography images, said computer modes being createdfrom image data of other subjects thereby to avoid the need to imagesaid subject under observation to create said computer model; andcomputer program code for applying scatter and attenuation correctionsto said emission tomography images using said aligned computer model asa guide, wherein said computer program code for aligning includes:computer program code for aligning a functional component of saidcomputer model simulating a SPECT or PET scan of said region of interestand for generating a set of spatial transformation parameters; andcomputer program code for aligning an anatomical component of saidcomputer model simulating a transmission scan of said region of interestusing said set of spatial transformation parameters.
 34. A method ofapplying scatter and attenuation correction to emission tomographyimages of a region of interest of a subject under observation comprisingthe steps of: aligning a three-dimensional computer model in the form ofa two-component atlas representing the density distribution within saidregion of interest with said emission tomography images; and applyingscatter and attenuation correction to said emission tomography imagesusing said aligned computer model as a guide.
 35. The method of claim 34wherein during said aligning step, a functional component of said atlasis firstly aligned with said emission tomography images to generate aset of spatial transformation parameters and thereafter, an anatomicalcomponent of said atlas is aligned with said emission tomography imagesusing said set of spatial transformation parameters.
 36. The method ofclaim 35 wherein said functional component simulates a SPECT or PET scanof said region of interest and wherein said anatomical componentsimulates a transmission scan of said region of interest.
 37. The methodof claim 36 further comprising the step of selecting an atlas from adatabase of atlases prior to performing said aligning step.
 38. Themethod of claim 34 wherein said computer model is created fromtransmission images or x-ray CT scans of the region of interest of othersubjects.
 39. The method of claim 38 wherein said transmission images orx-ray CT scans are taken from a variety of other subjects and averagedthereby to create said computer model.
 40. An emission tomographyimaging method where emission tomography images of a region of interestof a subject are taken for analysis and are corrected for scatter andattenuation, the method further comprising the step of: using athree-dimensional computer model in the form of a two-component atlasapproximating the density distribution within the region of interest asa guide to the application of scatter and attenuation correction. 41.The emission tomography imaging method of claim 40 wherein during saidaligning step, a functional component of said atlas is firstly alignedwith said emission tomography images to generate a set of spatialtransformation parameters and thereafter, an anatomical component ofsaid atlas is aligned with said emission tomography images using saidset of spatial transformation parameters.
 42. The emission tomographyimaging method of claim 41 wherein said functional component simulates aSPECT or PET scan of said region of interest and wherein said anatomicalcomponent simulates a transmission scan of said region of interest. 43.An emission tomography image processing system comprising: memorystoring emission tomography images of a region of interest of a subject;said memory also storing at least one three-dimensional computer modelof said region of interest, said computer model being a two-componentatlas representing the density distribution within said region ofinterest; and a processor for registering said computer model with saidemission tomography images and for applying scatter and attenuationcorrection to said emission tomography images using said registeredcomputer model as a guide.
 44. An emission tomography image processingsystem as defined in claim 43 wherein said processor firstly registers afunctional component of said atlas with said emission tomography imagesto generate a set of spatial transformation parameters and thenregisters an anatomical component of said atlas with said emissiontomography images using said set of spatial transformation parameters.45. An emission tomography image processing system as defined in claim44 wherein said functional component simulates a SPECT or PET scan ofsaid region of interest and wherein said anatomical component simulatesa transmission scan of said region of interest.
 46. An emissiontomography image processing system as defined in claim 45 wherein saidmemory stores a database of atlases.
 47. The method of claim 43 whereinsaid computer model is created from transmission images or x-ray CTscans of the region of interest of other subjects.
 48. The method ofclaim 47 wherein said transmission images or x-ray CT scans are takenfrom a variety of other subjects and averaged thereby to create saidcomputer model.
 49. An emission tomography imaging method comprising thesteps of: obtaining emission tomography images of a region of interestof a subject under observation; aligning a three-dimensional computermodel in the form of a two-component atlas representing the densitydistribution within said region of interest with said emissiontomography images without requiring said subject to be imaged to createsaid computer model; and applying scatter and attenuation correction tosaid emission tomography images using said aligned computer model as aguide.
 50. The method of claim 49 wherein during said aligning step, afunctional component of said atlas is firstly aligned with said emissiontomography images to generate a set of spatial transformation parametersand thereafter, an anatomical component of said atlas is aligned withsaid emission tomography images using said set of spatial transformationparameters.
 51. The method of claim 50 wherein said functional componentsimulates a SPECT or PET scan of said region of interest and whereinsaid anatomical component simulates a transmission scan of said regionof interest.
 52. The method of claim 51 wherein said region of interestis the head and wherein said functional component is the brain componentof a head atlas.
 53. The method of claim 51 wherein said region ofinterest is the heart, said functional component of said atlassimulating a cardiac image and said anatomical component of said atlasrepresenting anatomical features of the thorax.
 54. The method of claim53 wherein the anatomical features of the thorax include: soft-tissuessuch as the heart, liver, muscle, and fat; very low-density soft-tissuessuch as the lungs; and high-density tissues such as bone and cartilagein the ribs and spine.
 55. The method of claim 50 further comprising thestep of selecting an atlas from a database of atlases prior toperforming said aligning step.
 56. The method of claim 55 wherein saidselecting step is performed manually.
 57. The method of claim 55 whereinsaid selecting step is performed automatically based on the degree ofregistration of each atlas in said database with said emissiontomography images.
 58. The method of claim 57 wherein the degree ofregistration is determined by: performing a preliminary reconstructionof each atlas; and registering the atlas to the preliminaryreconstruction.
 59. The method of claim 57 further comprising the stepof combining multiple atlases to yield a resultant atlas that betterregisters with said emission tomography images.
 60. The method of claim55 wherein said database includes disease specific atlases, physicaltrait specific atlases and/or tracer or lesion specific atlases.